Parameter determination apparatus, parameter determination method and recording medium

ABSTRACT

A parameter determination apparatus ( 3 ) includes: a calculation unit ( 313 ) that is configured to calculate, based on a recognized result of a plurality of recognition target images by a recognition apparatus ( 2 ) that performs a recognition operation on the recognition target image ( 100 ,  200 ), an evaluation value for evaluating the recognized result; and a determination unit ( 314 ) that is configured to determine, based on the evaluation value, an image generation parameter ( 300 ,  301 ,  302   303   b ) that is used to generate the recognition target image.

TECHNICAL FIELD

The present disclosure relates to a technical field of a parameterdetermination apparatus, a parameter determination method and arecording medium that are configured to determine an image generationparameter that is used to generate an input image inputted to arecognition apparatus for recognize the input image.

BACKGROUND ART

A technique for automatically calculating (in other words, determining)a parameter for an image processing that is performed on an image isknown (for example, see a Patent Literature 1). Moreover, a techniquefor effectively finding out a condition for accurately performing aninformation processing using an image is known (for example, see aPatent Literature 2). Additionally, there is a Patent Literature 3 as abackground art document related to the present disclosure.

CITATION LIST Patent Literature

Patent Literature 1: JP2012-198680A

Patent Literature 2: WO2014/002398A1

Patent Literature 3: JP2017-130794A

SUMMARY Technical Problem

A recognition processing for recognizing an image is one example of aninformation processing using an image. For example, a face recognitionapparatus that recognizes a face of a person included in an image andauthenticates the person based on the recognized face is one example ofthe recognition processing. In this case, the image is inputted to arecognition apparatus, which recognizes the image, from an imagingapparatus such as a camera.

Here, the imaging apparatus that captures the image usually outputs theimage that is easy to be viewable by a person (namely, easy to be viewedby an eye of the person). This is because such a usage that the personsees the image is general as a usage of the image captured by theimaging apparatus. Thus, a parameter that specifies an opticalcharacteristic of the imaging apparatus and a parameter that specifies adetail of an image processing performed in the imaging apparatus are setto satisfy such a condition that the imaging apparatus outputs the imagethat is easy to be viewable by the person. On the other hand, the imagethat is easy to be viewable by the person is not always an image that iseasy to be recognized by the recognition apparatus. This is because therecognition apparatus uses the image as digital data. Thus, there is apossibility that the recognition apparatus is not capable of properlyrecognizing the image when the recognition processing is performed byusing the image outputted from the imaging apparatus as it is.

It is an example object of the present disclosure to provide a parameterdetermination apparatus, a parameter determination method and arecording medium that are configured to solve the above describedtechnical problem. As one example, it is an example object of thepresent disclosure to provide a parameter determination apparatus, aparameter determination method and a recording medium that areconfigured to determine an image generation parameter used to generatean image that is a target for a recognition operation so that arecognition apparatus is capable of performing the recognition operationon an image that is easy to be recognized by the recognition apparatus.

Solution to Problem

One example aspect of a parameter determination apparatus includes: acalculation unit that is configured to calculate, based on a recognizedresult of a plurality of recognition target images by a recognitionapparatus that performs a recognition operation on the recognitiontarget image, an evaluation value for evaluating the recognized result;and a determination unit that is configured to determine, based on theevaluation value, an image generation parameter that is used to generatethe recognition target image.

One example aspect of a parameter determination method includes:calculating, based on a recognized result of a plurality of recognitiontarget images by a recognition apparatus that recognizes the recognitiontarget image, an evaluation value for evaluating the recognized result;and determining, based on the evaluation value, an image generationparameter that is used to generate the recognition target image.

One example aspect of a recording medium is a recording medium on whicha computer program that allows a computer to execute a parameterdetermination method is recorded, the parameter determination methodincludes: calculating, based on a recognized result of a plurality ofrecognition target images by a recognition apparatus that recognizes therecognition target image, an evaluation value for evaluating therecognized result; and determining, based on the evaluation value, animage generation parameter that is used to generate the recognitiontarget image.

Effect

One example aspect of each of the parameter determination apparatus, theparameter determination method and the recording medium described aboveis capable of determining the image generation parameter used togenerate the image that is the target for the recognition operation sothat the recognition apparatus is capable of performing the recognitionoperation on the image that is easy to be recognized by the recognitionapparatus.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram that illustrates an entire configuration of arecognition system in a first example embodiment.

FIG. 2 is a block diagram that illustrates a configuration of an imagingapparatus in the first example embodiment.

FIG. 3 is a block diagram that illustrates a configuration of arecognition apparatus in the first example embodiment.

FIG. 4 is a block diagram that illustrates a configuration of aparameter determination apparatus in the first example embodiment.

FIG. 5 is a flow chart that illustrates a flow of a parameterdetermination operation that is performed by parameter determinationapparatus in the first example embodiment.

FIG. 6 is a block diagram that illustrates a configuration of arecognition apparatus in a second example embodiment.

FIG. 7 is a flow chart that illustrates a flow of a parameterdetermination operation that is performed by parameter determinationapparatus in the second example embodiment.

FIG. 8 is a block diagram that illustrates a configuration of aparameter determination apparatus in a third example embodiment.

FIG. 9 is a planar view that illustrates a candidate value of an imagegeneration parameter that should be set to the imaging apparatus or therecognition apparatus by the parameter determination operation.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Next, an example embodiment of a parameter determination apparatus, aparameter determination method and a recording medium will be describedwith reference to the drawings. In the below described description, arecognition system SYS to which the example embodiment of the parameterdetermination apparatus, the parameter determination method and therecording medium is applied will be described by using.

Recognition System SYS in First Example Embodiment

Firstly, the recognition system SYS in a first embodiment will bedescribed. Hereinafter, the recognition system SYS in the firstembodiment is referred to as a “recognition system SYSa”.

1) Configuration of Authentication System SYSa 1-1) Entire Configurationof Authentication System SYSa

Firstly, with reference to FIG. 1 , an entire configuration of therecognition system SYSa in the first example embodiment will bedescribed. FIG. 1 is a block diagram that illustrates the entireconfiguration of the recognition system SYSa in the first exampleembodiment.

As illustrated in FIG. 1 , the recognition system SYSa includes animaging apparatus 1, a recognition apparatus 2 and a parameterdetermination apparatus 3. The imaging apparatus 1, the recognitionapparatus 2 and the parameter determination apparatus 3 are connected tocommunicate with one another through a communication network 4. Thecommunication network 4 may include a wired network and may include awireless network.

The imaging apparatus 1 is an imaging apparatus that is configured tocapture an image of a person to generate a recognition target image 100in which the person is included. The imaging apparatus 1 transmits (inother words, inputs) the generated recognition target image 100 to therecognition apparatus 2 through the communication network 4.

The recognition apparatus 2 obtains (in other words, receives) therecognition target image 100 generate by the imaging apparatus 1 throughthe communication network 4. The recognition apparatus 2 performs arecognition operation for recognizing a face of the person included inthe recognition target image 100 based on the obtained recognitiontarget image 100 and authenticating the person included in therecognition target image 100 by using the recognized face.

The parameter determination apparatus 3 performs a parameterdetermination operation for determining an image generation parameter300 (specifically, determining a value of the image generation parameter300). The image generation parameter 300 is used (in other words,referred) by the imaging apparatus 1 to generate the recognition targetimage 100. The image generation parameter 300 specifies a detail of anoperation of the imaging apparatus 1 that generates the recognitiontarget image 100. Therefore, the imaging apparatus 1 generates therecognition target image 100 based on the image generation parameter 300generated by the parameter determination apparatus 3.

1-2) Configuration of Imaging Apparatus 1

Next, with reference to FIG. 2 , a configuration of the imagingapparatus 1 will be described. FIG. 2 is a block diagram thatillustrates the configuration of the imaging apparatus 1.

As illustrated in FIG. 2 , the imaging apparatus 1 includes: a camera11, an arithmetic apparatus 12 and a communication apparatus 13. Thecamera 11, the arithmetic apparatus 12 and the communication apparatus13 are interconnected through a data bus 14.

The camera 11 generates a captured image 101 in which a person isincluded by capturing an image of the person. An optical characteristicof the camera 11 is specified by an optical parameter 301 that is oneexample of the image generation parameter 300. In this case, the camera11 is a camera that has the optical characteristic specified by theoptical parameter 301 that is set (in other words, applied, reflected orregistered) in the camera 11. Therefore, a characteristic of thecaptured image 101 generated by the camera 11 is specified by theoptical parameter 301. Namely, the camera 11 generates the capturedimage 101 based on the optical parameter 301. Note that at least one ofan aperture value of the camera 11, a focus position (in other words, apoint of fucus) of the camera 11, a shutter speed of the camera 11 and asensitivity of the camera 11 is one example of the opticalcharacteristic of the camera 11 specified by the optical parameter 301.Thus, the optical parameter 301 may include at least one of a parameterthat specifies the aperture value of the camera 11, a parameter thatspecifies the focus position (in other words, the point of fucus) of thecamera 11, a parameter that specifies the shutter speed of the camera 11and a parameter that specifies the sensitivity of the camera 11.

The arithmetic apparatus 12 includes at least one of a CPU (CentralProcessing Unit) and GPU (Graphic Processing Unit), for example. Thearithmetic apparatus 12 reads a computer program. For example, thearithmetic apparatus 12 may read a computer program stored in anot-illustrated storage apparatus of the imaging apparatus 1. Forexample, the arithmetic apparatus 12 may read a computer program storedin a computer-readable non-transitory recording medium, by using anot-illustrated recording medium reading apparatus. The arithmeticapparatus 12 may obtain (namely, download or read) a computer programfrom a not-illustrated apparatus placed outside the imaging apparatus 1through the communication apparatus 13.

The arithmetic apparatus 12 executes the read computer program. As aresult, an image processing unit 121 for performing an image processingon the captured image 101 is implemented in the arithmetic apparatus 12as a logical functional block. Namely, the arithmetic apparatus 12 isconfigured to serve as a controller for implementing the imageprocessing unit 121.

The image processing unit 121 generates a recognition target image 100by performing a predetermined image processing on the captured image 101generated by the camera 11. A detail of the image processing performedby the image processing unit 121 is specified by a processing parameter302 that is one example of the image generation parameter 300. In thiscase, the image processing unit 121 generates the recognition targetimage 100 by performing the image processing, a detail of which isspecified by the processing parameter 302, on the captured image 101. Atleast one of a white balance correction processing for correcting awhite balance of the captured image 101, a brightness correctionprocessing for correcting a brightness of the captured image 101, acontrast correction processing for correcting a contrast of the capturedimage 101, a dehaze processing for improving an image quality of thecaptured image 101 a visibility of which is deteriorated by an influenceof a haze, a HDR (High Dynamic Range) processing for improving the imagequality of the captured image 101 by adjusting a dynamic range of thecaptured image 101, a denoise processing for improving the image qualityof the captured image 101 the visibility of which is deteriorated by anoise and a skeleton texture decomposition processing for diving thecaptured image 101 into a skeleton image and a texture image is oneexample of the image processing performed by the image processing unit121. Thus, the processing parameter 302 may include at least one of aparameter that specifies a detail of the white balance correctionprocessing, a parameter that specifies a detail of the brightnesscorrection processing, a parameter that specifies a detail of thecontrast correction processing, a parameter that specifies a detail ofthe dehaze processing, a parameter that specifies a detail of the HDRprocessing, a parameter that specifies a detail of the denoiseprocessing and a parameter that specifies a detail of the skeletontexture decomposition processing. The parameter that specifies a detailof the white balance correction processing may include at least one of aparameter that specifies whether or not the white balance correctionprocessing is performed, a parameter that specifies an intensity of thewhite balance correction processing, a parameter that specifies a limitvalue of a correction amount, a parameter that specifies a limit valueof a correction amount of a G (Green) component relative to a R (Red)component and a parameter that specifies a limit value of a correctionamount of a B (Blue) component relative to the R component, for example.The parameter that specifies a detail of the brightness correctionprocessing may include at least one of a parameter that specifieswhether or not the brightness correction processing is performed, aparameter that specifies an intensity of the brightness correctionprocessing, a parameter that specifies a target value of the brightnessand a parameter that specifies a limit value of a correction amount, forexample. The parameter that specifies a detail of the contrastcorrection processing may include at least one of a parameter thatspecifies whether or not the contrast correction processing isperformed, a parameter that specifies an intensity of the contrastcorrection processing, a parameter that specifies a limit value of acorrection amount to a relatively dark area and a parameter thatspecifies a limit value of a correction amount to a relatively lightarea, for example. The parameter that specifies a detail of the dehazeprocessing may include at least one of a parameter that specifieswhether or not the dehaze processing is performed, a parameter thatspecifies an intensity of the dehaze processing and a parameter thatspecifies a limit value of a correction amount of the captured image 101by the dehaze processing, for example. The parameter that specifies adetail of the HDR processing may include at least one of a parameterthat specifies whether or not the HDR processing is performed, aparameter that specifies an intensity of the HDR processing, a parameterthat specifies a target value of the brightness by the HDR processing, aparameter that specifies a limit value of a correction amount of thecaptured image 101 by the HDR processing and a parameter that specifiesa threshold value for identifying a black area, for example. Theparameter that specifies a detail of the denoise processing may includeat least one of a parameter that specifies whether or not the denoiseprocessing is performed and a parameter that specifies an intensity ofthe denoise processing, for example. The parameter that specifies adetail of the skeleton texture decomposition processing may include atleast one of a parameter that specifies whether or not the skeletontexture decomposition processing is performed and a parameter thatspecifies an intensity of the skeleton texture decomposition processing,for example.

The communication apparatus 13 is configured to communicate with therecognition apparatus 2 and the parameter determination apparatus 3through the communication network 4. In the first example embodiment,the communication apparatus 13 is configured to transmit the recognitiontarget image 100 to the recognition apparatus 2 through thecommunication network 4. Moreover, the communication apparatus 13 isconfigured to receive the image generation parameter 300 (specifically,the optical parameter 301 and the processing parameter 302) determinedby the parameter determination apparatus 3 through the communicationnetwork 4. The optical parameter 301 received by the communicationapparatus 13 is applied to the camera 11. Thus, the camera 11 is acamera that has the optical characteristic specified by the opticalparameter 301 that is received by the communication apparatus 13. Theprocessing parameter 302 received by the communication apparatus 13 isapplied to the image processing unit 121. Thus, the image processingunit 121 performs, on the captured image 101, the image processing adetail of which is specified by the processing parameter 302 that isreceived by the communication apparatus 13.

1-3) Configuration of Recognition Apparatus 2

Next, with reference to FIG. 3 , a configuration of the recognitionapparatus 2 will be described. FIG. 3 is a block diagram thatillustrates the configuration of the recognition apparatus 2.

As illustrated in FIG. 3 , the recognition apparatus 2 includes anarithmetic apparatus 21, a storage apparatus 22 and a communicationapparatus 23. The arithmetic apparatus 21, the storage apparatus 22 andthe communication apparatus 23 are interconnected through a data bus 24.

The arithmetic apparatus 21 includes at least one of a CPU and GPU, forexample. The arithmetic apparatus 21 reads a computer program. Forexample, the arithmetic apparatus 21 may read a computer program storedin the storage apparatus 22. For example, the arithmetic apparatus 21may read a computer program stored in a computer-readable non-transitoryrecording medium, by using a not-illustrated recording medium readingapparatus. The arithmetic apparatus 21 may obtain (namely, download orread) a computer program from a not-illustrated apparatus placed outsidethe recognition apparatus 2 through the communication apparatus 23. Thearithmetic apparatus 21 executes the read computer program. As a result,a logical functional block for performing an operation (specifically,the above described recognition operation) that should be performed bythe recognition apparatus 2 is implemented in the arithmetic apparatus21. Namely, the arithmetic apparatus 21 is configured to serve as acontroller for implementing the logical block for performing therecognition operation.

FIG. 3 illustrates one example of the logical functional block that isimplemented in the arithmetic apparatus 21 for performing therecognition operation. As illustrated in FIG. 3 , a recognition unit 211is implemented in the arithmetic apparatus 21 as the logical functionalblock. The recognition unit 211 recognizes (specifically, detects) theface of the person included in the recognition target image 100 based onthe recognition target image 100 transmitted from the imaging apparatus1. Note that the recognition unit 211 may recognize (detect) the face ofthe person included in the recognition target image 100 by using anexisting method of recognizing (detecting) a face of a person includedin an image. Furthermore, the recognition unit 211 authenticates theperson included in the recognition target image 100 by using therecognized face. The recognition unit 211 may authenticate the personincluded by using an existing method of authenticating a person based ona face of the person (namely, an existing face authentication method).Next, one example of the method of authenticating the person based onthe face of the person will be described briefly. The recognition unit211 searches, from a face authentication DB (DataBase) 220, a recordsatisfying a face authentication condition that is determined based on afeature amount of the authenticated face. The face authentication DB 220includes a plurality of records in each of which a feature amount of aface of one person is associated with an identification information foruniquely identifying the one person. In this case, the recognition unit211 searches the record satisfying the face authentication condition bycomparing the feature amount of the authenticated face with the featureamount included in the face authentication DB 220. For example, therecognition unit 211 may search, from the face authentication DB 220,the record satisfying the face authentication condition that it includesthe feature amount same as the feature amount of the authenticated face.When there is the record satisfying the face authentication condition inthe face authentication DB 220, the recognition unit 211 authenticatesthat the person included in the recognition target image 100 is theperson that is identified by the identification information included inthe record satisfying the face authentication condition. When there isnot the record satisfying the face authentication condition in the faceauthentication DB 220, the recognition unit 211 determines that theperson included in the recognition target image 100 is a person thatcannot be authenticated. Namely, the recognition unit 211 determinesthat the person included in the recognition target image 100 cannot beauthenticated.

The storage apparatus 22 is configured to store desired data. Forexample, the storage apparatus 22 may temporarily store the computerprogram that is executed by the arithmetic apparatus 21. The storageapparatus 22 may temporarily store data temporarily used by thearithmetic apparatus 21 when the arithmetic apparatus 21 executes thecomputer program. The storage apparatus 22 may store data stored for along term by the authentication apparatus 2. In the first exampleembodiment, the storage apparatus 22 is configured to store the abovedescribed face authentication DB 220. Furthermore, the storage apparatus22 is configured to store an image DB (DataBase) 221 for accumulating(namely, storing, recording or containing) the recognition target image100 transmitted from the imaging apparatus 1. Furthermore, the storageapparatus 22 is configured to store a recognized result DB 222 foraccumulating a recognized result information that indicates a result ofthe recognition operation by the recognition unit 211 (for example, aninformation related to a recognized result of the person included in therecognition target image 100 and an information related to anauthenticated result of the recognized person). Note that the storageapparatus 22 may include at least one of a RAM (Random Access Memory), aROM (Read Only Memory), a hard disk apparatus, a magneto-optical disc, aSSD (Solid State Drive) and a disk array apparatus. Namely, the storageapparatus 22 may include a non-transitory recording medium.

The communication apparatus 23 is configured to communicate with theimaging apparatus 1 and the parameter determination apparatus 3 throughthe communication network 4. In the first example embodiment, thecommunication unit 23 is configured to obtain (namely, receive) therecognition target image 100 transmitted from the imaging apparatus 1through the communication network 4. Furthermore, the communication unit23 is configured to transmit the recognized result informationaccumulated in the recognized result DB 222 to the parameterdetermination apparatus 3 through the communication network 4. Theparameter determination apparatus 3 determines the image generationparameter 300 based on the recognized result information.

1-4) Configuration of Parameter Determination Apparatus 3

Next, with reference to FIG. 4 , a configuration of the parameterdetermination apparatus 3 will be described. FIG. 4 is a block diagramthat illustrates the configuration of the parameter determinationapparatus 3.

As illustrated in FIG. 4 , the parameter determination apparatus 3includes an arithmetic apparatus 31, a storage apparatus 32 and acommunication apparatus 33. The arithmetic apparatus 31, the storageapparatus 32 and the communication apparatus 33 are interconnectedthrough a data bus 34.

The arithmetic apparatus 31 includes at least one of a CPU and GPU, forexample. The arithmetic apparatus 31 reads a computer program. Forexample, the arithmetic apparatus 31 may read a computer program storedin the storage apparatus 32. For example, the arithmetic apparatus 31may read a computer program stored in a computer-readable non-transitoryrecording medium, by using a not-illustrated recording medium readingapparatus. The arithmetic apparatus 31 may obtain (namely, download orread) a computer program from a not-illustrated apparatus placed outsidethe parameter determination apparatus 3 through the communicationapparatus 23. The arithmetic apparatus 31 executes the read computerprogram. As a result, a logical functional block for performing anoperation (specifically, the above described parameter determinationoperation) that should be performed by the parameter determinationapparatus 3 is implemented in the arithmetic apparatus 31. Namely, thearithmetic apparatus 31 is configured to serve as a controller forimplementing the logical block for performing the parameterdetermination operation.

FIG. 4 illustrates one example of the logical functional block that isimplemented in the arithmetic apparatus 31 for performing the parameterdetermination operation. As illustrated in FIG. 4 , a parameter settingunit 311, a recognized result obtaining unit 312, an evaluation unit 313and a parameter determination unit 314 are implemented in the arithmeticapparatus 31 as the logical functional blocks. Incidentally, a detail ofan operation of each of the parameter setting unit 311, the recognizedresult obtaining unit 312, the evaluation unit 313 and the parameterdetermination unit 314 will be described in detail later by using FIG. 5and so on, however, an overview thereof will be described briefly here.The parameter setting unit 311 set the image generation parameter 300 ofthe imaging apparatus 1 by transmitting the image generation parameter300 that should be set to the imaging apparatus 1. The imaging apparatus1 generates the recognition target image 100 by using the imagegeneration parameter 300 set by the parameter setting unit 311. Therecognized result obtaining unit 312 obtains the recognized resultinformation indicating the result of the recognition operation (namely,the recognized result information accumulated in the recognized resultDB 222 in the storage apparatus 22) from the recognition apparatus 2.The evaluation unit 313 generates, based on the recognized resultinformation, an evaluation value for evaluating whether or not the imagegeneration parameter 300 that is used by the imaging apparatus 1 togenerate the recognition target image 100 (namely, the image generationparameter 300 that is actually set to the imaging apparatus 1) isproper. The parameter determination unit 314 determines (in other words,calculates) a value of the image generation parameter 300 that should beset to the imaging apparatus 1 based on the evaluation value calculatedby the evaluation unit 313.

The storage apparatus 32 is configured to store desired data. Forexample, the storage apparatus 32 may temporarily store the computerprogram that is executed by the arithmetic apparatus 31. The storageapparatus 32 may temporarily store data temporarily used by thearithmetic apparatus 31 when the arithmetic apparatus 31 executes thecomputer program. The storage apparatus 32 may store data stored for along term by the parameter determination apparatus 3. In the firstexample embodiment, the storage apparatus 32 is configured to store aparameter DB 321 for accumulating (namely, storing, recording orcontaining) an information related to the image generation parameter 300determined by the parameter determination apparatus 3. Furthermore, thestorage apparatus 32 is configured to store a ground truth DB 322 foraccumulating ground truth data that is compared with the recognizedresult information for calculating the above described evaluation value(for example, a F-measure described below). Note that the storageapparatus 32 may include at least one of a RAM (Random Access Memory), aROM (Read Only Memory), a hard disk apparatus, a magneto-optical disc, aSSD (Solid State Drive) and a disk array apparatus. Namely, the storageapparatus 32 may include a non-transitory recording medium.

The communication apparatus 33 is configured to communicate with theimaging apparatus 1 and the recognition apparatus 2 through thecommunication network 4. In the first example embodiment, thecommunication apparatus 33 is configured to obtain (namely, receive) therecognized result information transmitted from the recognition apparatus2 through the communication network 4. Furthermore, the communicationunit 23 is configured to transmit, to the imaging apparatus, the imagegeneration parameter 300 that should be set to the imaging apparatus 1through the communication network 4 under the control of the parametersetting unit 311. The image generation parameter 300 transmitted by thecommunication unit 33 is applied to the imaging apparatus 1. Namely, theimaging apparatus 1 generates the recognition target image 100 by usingthe image generation parameter 300 transmitted by the communication unit33.

2) Operation of Recognition System SYSa

Next, with reference to FIG. 5 , an operation of the recognition systemSYS in the first example embodiment will be described. Especially, inthe below described description, the parameter determination operationthat is performed by the parameter determination apparatus 3 will bedescribed. FIG. 5 is s flowchart that illustrates a flow of theparameter determination operation that is performed by the parameterdetermination apparatus 3.

As illustrated in FIG. 5 , firstly, the parameter setting unit 311initializes a variable number r to be zero (a step S301). Then, theparameter setting unit 311 sets the image generation parameters 300 ofthe imaging apparatus 1 (specifically, the optical parameter 301 of thecamera 11 and the processing parameter 302 of the image processing unit121) to be initial values (a step S310). Note that either one of aplurality of candidate values for the image generation parameter 300that should be set to the imaging apparatus 1 in the parameterdetermination operation may be used as the initial value of the imagegeneration parameter 300. The plurality of candidate values may includea recommended value that is set to the imaging apparatus 1 in advance.

Then, the parameter determination unit 314 controls the imagingapparatus 1 to generate the recognition target images 100, the number ofwhich is equal to or larger than a predetermine number, based on theimage generation parameters 300 set at the step S310 (a step S311).Namely, the parameter determination unit 314 controls the camera 11,which has the optical characteristic that is specified by the opticalparameter 301 set at the step S310, to capture (namely, generate) thecaptured images 101 the number of which is equal to or larger than thepredetermined number. Furthermore, the parameter determination unit 314controls the image processing unit 121 to generate the recognitiontarget images 100 the number of which is equal to or larger than thepredetermine number by performing the image processing, the detail ofwhich is specified by the processing parameter 302 set at the step S310,on the captured images 101 the number of which is equal to or largerthan the predetermined number. The recognition apparatus 2 obtains therecognition target images 100 generated by the imaging apparatus 1 byusing the communication apparatus 23 and accumulates them in the imageDB 221 of the storage apparatus 22.

Incidentally, in the parameter determination operation, the camera 11may capture the image of the person in a situation where the person isactually located in front of the camera 11. Alternatively, the camera 11may capture an image or an image of a model in a situation where theimage or the model that imitates the person is located in front of thecamera 11. Furthermore, an information related to the person captured bythe camera 11 (alternatively, the person that is imitated by the imageor the model captured by the camera 11) is accumulated as the groundtruth data in the ground truth DB 322. Namely, in the ground truth DB322, the ground truth data indicating the person included in eachrecognition target image 100 transmitted from the imaging apparatus 1 tothe recognition apparatus 2 (for example, the identification informationfor uniquely identifying the person), the number of which is equal tothe number of the recognition target image 100 used by the parameterdetermination operation, are accumulated.

After the recognition target image 100 the number of which is equal toor larger than the predetermined number are generated, the parameterdetermination unit 314 controls the recognition apparatus 2 (especially,the recognition unit 211) to perform the above described recognitionoperation on each of the recognition target images 100 the number ofwhich is equal to or larger than the predetermined number generated atthe step S311 (a step S312). The recognition apparatus 2 accumulates therecognized result information indicating the result of the recognitionoperation in the recognized result DB 222 of the storage apparatus 22.

After the recognition operation on the recognition target images 100 thenumber of which is equal to or larger than the predetermined number iscompleted, the recognized result obtaining unit 312 obtains therecognized result information indicating the result of the recognitionoperation at the step S312 from the recognition apparatus 2 through thecommunication apparatus 33 (a step S313).

Then, the evaluation unit 313 calculates the evaluation value forevaluating whether or not the image generation parameters 300 that areactually set to the imaging apparatus 1 at the step S310 is proper basedon the recognized result information obtained at the step S313 and theground truth data stored in the ground truth DB 322 (a step S314).Namely, the evaluation unit 313 calculates the evaluation value forevaluating the recognition operation that is performed on therecognition target image 100 generated based on the image generationparameters 300 set at the step S310.

For example, the evaluation unit 313 may calculate the F-measure as theevaluation value. The F-measure is an evaluation value that isdetermined based on a precision and a recall. Specifically, theF-measure is an evaluation value that is defined by an equation“F-measure = (2 × precision × recall) / (precision + recall)”. Theprecision indicates a ratio of the number of the recognition targetimage 100 in which the authentication of the person succeeds relative tothe number of the recognition target images 100 from which the face ofthe person is detected. The recall indicates a ratio of the number ofthe recognition target image 100 in which the authentication of theperson succeeds relative to the number of the recognition target images100 in which the person is included (namely, a total number of therecognition target images 100 in which the person should beauthenticated, and a total number of the ground truth data correspondingto the recognition target images 100).

“The recognition target image 100 in which the authentication of theperson succeeds” in the present example embodiment means “therecognition target image 100 that allows the recognition unit 211 tocorrectly authenticate the person included in the recognition targetimage 100 to be that person himself”. Namely, “the recognition targetimage 100 that makes the recognition unit 211 authenticate the personincluded in the recognition target image 100 to be another person(namely, the recognition target image 100 that is incorrectlyauthenticated by the recognition unit 211)” is not included in “therecognition target image 100 in which the authentication of the personsucceeds”. Specifically, when the recognized result information relatedto one recognition target image 100 including one person indicates suchan authenticated result that “the person included in the one recognitiontarget image 100 is authenticated to be the one person”, the onerecognition target image 100 is included in “the recognition targetimage 100 in which the authentication of the person succeeds”. On theother hand, when the recognized result information related to onerecognition target image 100 including one person indicates such anauthenticated result that “the person included in the one recognitiontarget image 100 is authenticated to be another person that is differentfrom the one person”, the one recognition target image 100 is notincluded in “the recognition target image 100 in which theauthentication of the person succeeds”. Thus, the ground truth DB 322that is used to calculate the evaluation value accumulates the groundtruth data indicating that the person included in the one recognitiontarget image 100 is the one person. In this case, the evaluation unit313 is capable of calculating “the number of the recognition targetimage 100 in which the authentication of the person succeeds” by usingan evaluated result information and the ground truth DB 322, and as aresult, is capable of correctly calculating the F-measure (namely, theevaluation value).

Alternatively, the evaluation unit 313 may calculate, as the evaluationvalue, a value that is different from the F-measure. For example, theevaluation unit 313 may calculate the above described precision itselfas the evaluation value. For example, the evaluation unit 313 maycalculate the above described recall itself as the evaluation value. Forexample, the evaluation unit 313 may calculate the evaluation valuedetermined based on the above described precision. For example, theevaluation unit 313 may calculate the evaluation value determined basedon the above described recall. The ground truth DB 322 may not be useddepending on the calculated evaluation value. In this case, the storageapparatus 32 may not store the ground truth DB 322.

Then, the parameter determination unit 314 determines whether or not thevariable number r is zero (a step S315).

As a result of the determination at the step S315, when it is determinedthat the variable number r is zero (the step S315: Yes), the parameterdetermination unit 314 records, in the parameter DB 321, the informationrelated to the image generation parameters 300 set at the step S310 (astep S317). The information related to the image generation parameters300 may include an information indicating the value of the imagegeneration parameters 300 set at the step S310 and an informationindicating the evaluation value calculated at the step S314, forexample.

The information related to the image generation parameters 300 recordedin the parameter DB 321 corresponds to the information related to theimage generation parameters 300 that should be used by the imagingapparatus 1 to generate the recognition target image 100 (namely, theimage generation parameters 300 that should be set to the imagingapparatus 1). Thus, after the parameter determination operationillustrated in FIG. 5 is completed, the values of the image generationparameters 300 recorded in the parameter DB 321 are actually set to theimaging apparatus 1. Namely, after the parameter determination operationillustrated in FIG. 5 is completed, the imaging apparatus 1 generatesthe recognition target image 100 based on the image generationparameters 300 recorded in the parameter DB 321.

Then, the parameter setting unit 311 determines whether or not allcandidate values for the image generation parameter 300 that should beset to the imaging apparatus 1 in the parameter determination operationis actually set to the imaging apparatus 1 (a step S318). For example,in a situation where a first candidate value to a fifth candidate valueshould be set to the imaging apparatus 1 as the image generationparameters 300 in the parameter determination operation, the parametersetting unit 311 determines whether or not each of the first candidatevalue to the fifth candidate value is actually set to the imagingapparatus 1 as the image generation parameter 300. When at least one ofthe first candidate value to the fifth candidate value is not yet set tothe imaging apparatus 1 as the image generation parameter 300, theparameter setting unit 311 determines that all of the candidate valuesfor the image generation parameters 300 that should be set to theimaging apparatus 1 in the parameter determination operation are notactually set to the imaging apparatus 1.

As a result of the determination at the step S318, when it is determinedthat all candidate values for the image generation parameters 300 thatshould be set to the imaging apparatus 1 in the parameter determinationoperation are not yet set to the imaging apparatus 1 (the step S318:No), the parameter setting unit 311 increments the variable number r byone (a step S319), and set the image generation parameter 300 having newvalue (namely, new candidate value for the image generation parameter300) to the imaging apparatus 1 (the step S310). Then, the processes ofthe step S311 and after the step S311 are repeated.

On the other hand, as a result of the determination at the step S318,when it is determined that all candidate values for the image generationparameters 300 that should be set to the imaging apparatus 1 in theparameter determination operation are already set to the imagingapparatus 1 (the step S318: Yes), the parameter determination apparatus3 ends the parameter determination operation illustrated in FIG. 5 .

On the other hand, as a result of the determination at the step S315,when it is determined that the variable number r is not zero (the stepS315: No), the information related to the image generation parameters300 is already recorded in the parameter DB 321. In this case, theparameter determination unit 314 determines whether or not theevaluation value that is newly calculated at the step S314 is betterthan the evaluation value that is recorded in the parameter DB 321 withit to be associated with the information indicating the image generationparameters 300 (a step S316). Namely, the parameter determination unit314 determines whether or not the evaluation value corresponding to theimage generation parameters 300 that are newly set at the step S301 isbetter than the evaluation value corresponding to the image generationparameters 300 that is recorded in the parameter DB 321.

Note that the evaluation value is an index value that is better as therecognition operation is better (for example, a recognition accuracy ofthe face of the person is better and / or an authentication accuracy ofthe person is better). Therefore, at the step S316, it can be said thatthe parameter determination unit 314 determines whether or not a resultof the recognition operation using the recognition target image 100generated based on the image generation parameters 300 that are newlyset at the step S301 is better than a result of the recognitionoperation using the recognition target image 100 generated based on theimage generation parameters 300 that are recorded in the parameter DB321. For example, it can be said that the parameter determination unit314 determines whether or not the recognition accuracy of the face ofthe person using the recognition target image 100 generated based on theimage generation parameters 300 that are newly set at the step S301 isbetter than the recognition accuracy of the face of the person using therecognition target image 100 generated based on the image generationparameters 300 that are recorded in the parameter DB 321. For example,it can be said that the parameter determination unit 314 determineswhether or not the authentication accuracy of the person using therecognition target image 100 generated based on the image generationparameters 300 that are newly set at the step S301 is better than theauthentication accuracy of the person using the recognition target image100 generated based on the image generation parameters 300 that arerecorded in the parameter DB 321.

As a result of the determination at the step S316, when it is determinedthat the evaluation value is better (the step S316: Yes), it isestimated that the result of the recognition operation using therecognition target image 100 generated based on the image generationparameters 300 that are newly set at the step S310 is better than theresult of the recognition operation using the recognition target image100 generated based on the image generation parameters 300 that arerecorded in the parameter DB 321. For example, it is estimated that therecognition accuracy of the face of the person using the recognitiontarget image 100 generated based on the image generation parameters 300that are newly set at the step S310 is better than the recognitionaccuracy of the face of the person using the recognition target image100 generated based on the image generation parameters 300 that arerecorded in the parameter DB 321. For example, it is estimated that theauthentication accuracy of the person using the recognition target image100 generated based on the image generation parameters 300 that arenewly set at the step S310 is better than the authentication accuracy ofthe person using the recognition target image 100 generated based on theimage generation parameters 300 that are recorded in the parameter DB321. Thus, it is estimated that the image generation parameters 300 thatare newly set at the step S310 is more suitable for the purpose ofperforming the proper recognition operation (for example, therecognition operation having higher accuracy) than the image generationparameters 300 that are recorded in the parameter DB 321. In this case,the parameter determination unit 314 newly records, in the parameter DB321, the information related to the image generation parameters 300 thatare newly set at the step S310 (the step S317). Namely, the parameterdetermination unit 314 rewrites (namely, updates) the informationrelated to the image generation parameters 300 that are recorded in theparameter DB 321 by the information related to the image generationparameters 300 that are newly set at the step S310.

On the other hand, as a result of the determination at the step S316,when it is determined that the evaluation value is not better (the stepS316: No), it is estimated that the result of the recognition operationusing the recognition target image 100 generated based on the imagegeneration parameters 300 that are recorded in the parameter DB 321 isbetter than the result of the recognition operation using therecognition target image 100 generated based on the image generationparameters 300 that are newly set at the step S310. For example, it isestimated that the recognition accuracy of the face of the person usingthe recognition target image 100 generated based on the image generationparameters 300 that are recorded in the parameter DB 321 is better thanthe recognition accuracy of the face of the person using the recognitiontarget image 100 generated based on the image generation parameters 300that are newly set at the step S310. For example, it is estimated thatthe authentication accuracy of the person using the recognition targetimage 100 generated based on the image generation parameters 300 thatare recorded in the parameter DB 321 is better than the authenticationaccuracy of the person using the recognition target image 100 generatedbased on the image generation parameters 300 that are newly set at thestep S310. Thus, it is estimated that the image generation parameters300 that are recorded in the parameter DB 321 is more suitable for thepurpose of performing the proper recognition operation (for example, therecognition operation having higher accuracy) than the image generationparameters 300 that are newly set at the step S310. In this case, theparameter determination unit 314 does not newly record, in the parameterDB 321, the information related to the image generation parameters 300that are newly set at the step S310. Namely, the information related tothe image generation parameters 300 that are recorded in the parameterDB 321 is kept being recorded in the parameter DB 321 as it is.

3) Technical Effect of Recognition System SYSa

As described above, the recognition system SYSa (especially, theparameter determination apparatus 3) in the first example embodiment iscapable of determining the image generation parameter 300 by using therecognized result information indicating the recognized result of therecognition operation. Namely, the parameter determination apparatus 3is capable of determining the value of the image generation parameter300 that should be set to the imaging apparatus 1. Thus, the parameterdetermination apparatus 3 is capable of determining the value of theimage generation parameter 300 that realizes the proper recognitionoperation (for example, the recognition operation having higherrecognition accuracy and / or authentication accuracy). Namely, theparameter determination apparatus 3 is capable of determining the valueof the image generation parameter 300 by which the recognition targetimage 100 that is easy to be recognized by the recognition apparatus 2is generable. The parameter determination apparatus 3 is capable ofdetermining the value of the image generation parameter 300 that is usedto generate the recognition target image 100 so that the recognitionapparatus 2 performs the recognition operation by using the recognitiontarget image 100 that is easy to be recognized by the recognitionapparatus 2. Thus, when the parameter determination operation isperformed, the imaging apparatus 1 is capable of generating therecognition target image 100 that is easier to be recognized by therecognition apparatus 2, compared to the case where the parameterdetermination operation is not performed. Furthermore, when theparameter determination operation is performed, the recognitionapparatus 2 is capable of performing the more proper recognitionoperation by using the recognition target image 100 that is easier to berecognized by the recognition apparatus 2, compared to the case wherethe parameter determination operation is not performed.

Note that the parameter determination apparatus 3 may perform theparameter determination operation before an operation of theauthentication system SYSa starts. Alternatively, the parameterdetermination apparatus 3 may perform the parameter determinationoperation after the operation of the authentication system SYSa starts.

Moreover, as described above, the image generation parameters 300includes a plurality of types of parameters. In this case, the parameterdetermination apparatus 3 may determine the plurality of types ofparameters in sequence. For example, the parameter determinationapparatus 3 may determine a first type of parameter (for example, theparameter that specifies the aperture value of the camera 11), and then,may determine a second type of parameter (for example, the parameterthat specifies the detail of the white balance correction processing).

Moreover, the parameter determination apparatus 3 may determine theprocessing parameter 302 of the image processing unit 121 afterdetermining the optical parameter 301 of the camera 11. This is becausethe image processing unit 121 performs the image processing on thecaptured image 101 captured by the camera 11, and thus, there is apossibility that the value of the processing parameter 302 is needed tobe changed when the value of the optical parameter 301 is changed. Thus,when the processing parameter 302 is determined after the opticalparameter 301 is determined, the parameter determination apparatus 3 iscapable of determining the processing parameter 302 in a situation wherethe value of the optical parameter 301 is fixed. Thus, the parameterdetermination apparatus 3 is capable of determining the image generationparameter 300 relatively effectively.

Recognition System SYS in Second Example Embodiment

Next, the recognition system SYS in a second example embodiment will bedescribed. Hereinafter, the recognition system SYS in the second exampleembodiment is referred to as a “recognition system SYSb”.

1) Configuration of Recognition System SYSb

Firstly, a configuration of the recognition system SYSb in a secondexample embodiment will be described. The system SYSb in the secondexample embodiment is different from the recognition system SYSa in thefirst example embodiment in that it includes a recognition apparatus 2 binstead of the recognition apparatus 2. Another feature of therecognition system SYSb may be same as another feature of therecognition system SYSa. Thus, in the below described description, withreference to FIG. 6 , a configuration of the recognition apparatus 2 bin the second example embodiment will be described. FIG. 6 is a blockdiagram that illustrates the configuration of the recognition apparatus2 b in the second example embodiment. Note that a detailed descriptionof the component that is already described is omitted by assigning thesame reference number thereto. Moreover, a detailed description of theoperation that is already described is omitted by assigning the samestep number thereto.

As illustrated in FIG. 6 , the recognition apparatus 2 b is differentfrom the above described recognition apparatus 2 in that an imageprocessing unit 212 b is implemented in the arithmetic apparatus 21.Another feature of the recognition apparatus 2 b may be same as anotherfeature of the recognition apparatus 2.

The image processing unit 212 b generates a recognition target image 200by performing a predetermined image processing on the recognition targetimage 100 obtained from the imaging apparatus 1. The recognition targetimage 200 is used for the recognition operation by the recognition unit211. Namely, in the second example embodiment, the recognition unit 211performs the recognition operation by using the recognition target image200 instead of the recognition target image 100. Namely, the recognitionunit 211 recognizes the face of the person included in the recognitiontarget image 200 and authenticates the person included in therecognition target image 200 based on the recognized face.

A detail of the image processing performed by the image processing unit212 b is specified by a processing parameter 303 b that is one exampleof the image generation parameter 300. In this case, the imageprocessing unit 212 b generates the recognition target image 200 byperforming the image processing, a detail of which is specified by theprocessing parameter 303 b, on the recognition image 100. The imageprocessing unit 212 b may perform the image processing a type of whichis same as that of the image processing performed by the imageprocessing unit 121 of the above described imaging apparatus 1. Forexample, the image processing unit 212 b may perform at least one of thewhite balance correction processing, the brightness correctionprocessing, the contrast correction processing, the dehaze processing,the HDR processing, the denoise processing and the skeleton texturedecomposition processing. In this case, the processing parameter 303 bmay include at least one of a parameter that specifies a detail of thewhite balance correction processing, a parameter that specifies a detailof the brightness correction processing, a parameter that specifies adetail of the contrast correction processing, a parameter that specifiesa detail of the dehaze processing, a parameter that specifies a detailof the HDR processing, a parameter that specifies a detail of thedenoise processing and a parameter that specifies a detail of theskeleton texture decomposition processing, as with the above describedprocessing parameter 302. Alternatively, the image processing unit 212 bmay perform the image processing the type of which is different fromthat of the image processing performed by the image processing unit 121of the above described imaging apparatus 1.

2) Operation of Recognition System SYSb

Next, an operation of the recognition system SYSb in the second exampleembodiment will be described. Especially, in the below describeddescription, a parameter determination operation that is performed bythe parameter determination apparatus 3 will be described. In the secondexample embodiment, not only the parameter related to the imagingapparatus 1 (specifically, the optical parameter 301 and the processingparameter 302) described in the first example embodiment but also theparameter related to the recognition apparatus 2 (specifically, theprocessing parameter 303 b) are used as the image generation parameters300. Thus, the parameter determination apparatus 3 determines theprocessing parameter 302 related to the recognition apparatus 2 inaddition to or instead of determining the optical parameter 301 and theprocessing parameter 302 related to the imaging apparatus 1 byperforming the above described determination operation in the firstexample embodiment. Therefore, in the below described description, withreference to FIG. 7 , the parameter determination operation fordetermining the processing parameter 302 related to the recognitionapparatus 2 will be described. FIG. 7 is s flowchart that illustrates aflow of the parameter determination operation for determining theprocessing parameter 302 related to the recognition apparatus 2. Notethat the image generation parameter 300 means the processing parameter303 b in the below described description, when there is no specialnotation.

As illustrated in FIG. 7 , the parameter setting unit 311 initializes avariable number r to be zero (a step S302). Then, the parameterdetermination unit 314 controls the imaging apparatus 1 to generate therecognition target images 100, the number of which is equal to or largerthan a predetermine number (a step S321). Note that the process at thestep S321 may be same as the process at the step S311. However, in thestep S321, when the optical parameter 301 and the processing parameter302 are already determined, the parameter determination unit 314 maycontrol the imaging apparatus 1 to generate the recognition targetimages 100, the number of which is equal to or larger than thepredetermine number, based on the optical parameter 301 and theprocessing parameter 302 that are already determined. Alternatively,when the optical parameter 301 and the processing parameter 302 are notyet determined, the parameter determination unit 314 may control theimaging apparatus 1 to generate the recognition target images 100, thenumber of which is equal to or larger than the predetermine number,based on the default optical parameter 301 and processing parameter 302.

After the recognition target image 100 the number of which is equal toor larger than the predetermined number are generated, the parametersetting unit 311 sets the image generation parameter 300 of therecognition apparatus 2 (specifically, the processing parameter 302 ofthe image processing unit 212 b) to be initial values (a step S320).Note that either one of a plurality of candidate values for the imagegeneration parameter 300 that should be set to the recognition apparatus2 in the parameter determination operation may be used as the initialvalue of the image generation parameter 300. The plurality of candidatevalues may include a recommended value that is set to the recognitionapparatus 2 in advance.

Then, the parameter determination unit 314 controls the image processingunit 212 b of the recognition apparatus 2 to perform the imageprocessing, the detail of which is specified by the image generationparameter 300 set at the step S320, on each of the recognition targetimages 100, the number of which is equal to or larger than thepredetermined number, generated at the step S321 (a step S331). As arust, image processing unit 212 b generates the recognition targetimages 200 the number of which is equal to or larger than thepredetermined number.

Then, the parameter determination unit 314 controls the recognitionapparatus 2 (especially, the recognition unit 211) to perform the abovedescribed recognition operation on each of the recognition target images200 the number of which is equal to or larger than the predeterminednumber generated at the step S331 (a step S322). The recognized resultinformation indicating the result of the recognition operation isaccumulated in the recognized result DB 222 of the storage apparatus 22.

After the recognition operation on the recognition target images 200 thenumber of which is equal to or larger than the predetermined number iscompleted, the recognized result obtaining unit 312 obtains therecognized result information indicating the result of the recognitionoperation at the step S322 from the recognition apparatus 2 through thecommunication apparatus 33 (a step S323).

Then, the evaluation unit 313 calculates an evaluation value forevaluating whether or not the image generation parameter 300 that isactually set (in other words, applied) to the recognition apparatus 2 atthe step S322 is proper based on the recognized result informationobtained at the step S323 (a step S324). Namely, the evaluation unit 313calculates an evaluation value for evaluating the recognition operationthat is performed on the recognition target image 200 generated based onthe image generation parameter 300 set at the step S320. Note that theevaluation value used in the second example embodiment may be same asthe evaluation value used in the first example embodiment, and thus, adetailed description thereof is omitted. Namely, the process at the stepS324 may be same as the process at the above described step S314.However, in the second example embodiment, the precision indicates aratio of the number of the recognition target image 200 in which theauthentication of the person succeeds relative to the number of therecognition target images 200 from which the face of the person isdetected, and the recall indicates a ratio of the number of therecognition target image 200 in which the authentication of the personsucceeds relative to the number of the recognition target images 200 inwhich the person is included (namely, a total number of the recognitiontarget images 200 in which the person should be authenticated, and atotal number of the ground truth data corresponding to the recognitiontarget images 100 that are sources of the recognition target images200), because the recognition operation is performed on the recognitiontarget image 200 instead of the recognition target image 100.

Then, the parameter determination unit 314 determines whether or not thevariable number r is zero (a step S325). As a result of thedetermination at the step S325, when it is determined that the variablenumber r is zero (the step S325: Yes), the parameter determination unit314 records, in the parameter DB 321, the information related to theimage generation parameter 300 set at the step S320 (a step S327). Theinformation related to the image generation parameter 300 may include aninformation indicating the value of the image generation parameter 300set at the step S320 and an information indicating the evaluation valuecalculated at the step S324, for example.

The information related to the image generation parameter 300 recordedin the parameter DB 321 corresponds to the information related to theimage generation parameter 300 that should be used by the recognitionapparatus 2 to generate the recognition target image 200 (namely, theimage generation parameter 300 that should be set to the recognitionapparatus 2). Thus, after the parameter determination operationillustrated in FIG. 7 is completed, the values of the image generationparameter 300 recorded in the parameter DB 321 is actually set to therecognition apparatus 2. Namely, after the parameter determinationoperation illustrated in FIG. 7 is completed, the recognition apparatus2 generates the recognition target image 200 based on the imagegeneration parameter 300 recorded in the parameter DB 321.

Then, the parameter setting unit 311 determines whether or not allcandidate value for the image generation parameter 300 that should beset to the recognition apparatus 2 in the parameter determinationoperation is actually set to the recognition apparatus 2 (a step S328).For example, in a situation where a first candidate value to a fifthcandidate value should be set to the recognition apparatus 2 as theimage generation parameters 300 in the parameter determinationoperation, the parameter setting unit 311 determines whether or not eachof the first candidate value to the fifth candidate value is actuallyset to the recognition apparatus 2 as the image generation parameter300.

As a result of the determination at the step S328, when it is determinedthat all candidate value for the image generation parameter 300 thatshould be set to the recognition apparatus 2 in the parameterdetermination operation is not yet set to the recognition apparatus 2(the step S328: No), the parameter setting unit 311 increments thevariable number r by one (a step S329), and set the image generationparameter 300 having new value (namely, new candidate value for theimage generation parameter 300) to the recognition apparatus 2 (the stepS320). Then, the processes of the step S331 and after the step S331 arerepeated.

On the other hand, as a result of the determination at the step S328,when it is determined that all candidate value for the image generationparameter 300 that should be set to the recognition apparatus 2 in theparameter determination operation is already set to the recognitionapparatus 2 (the step S328: Yes), the parameter determination apparatus3 ends the parameter determination operation illustrated in FIG. 7 .

On the other hand, as a result of the determination at the step S325,when it is determined that the variable number r is not zero (the stepS325: No), the information related to the image generation parameter 300is already recorded in the parameter DB 321. In this case, the parameterdetermination unit 314 determines whether or not the evaluation valuethat is newly calculated at the step S324 is better than the evaluationvalue that is recorded in the parameter DB 321 with it to be associatedwith the information indicating the image generation parameter 300 (astep S326). Namely, the parameter determination unit 314 determineswhether or not the evaluation value corresponding to the imagegeneration parameter 300 that is newly set at the step S320 is betterthan the evaluation value corresponding to the image generationparameter 300 that is recorded in the parameter DB 321. Note that theevaluation value being better at the step S326 may mean the recognitionaccuracy of the face of the person and / or the authentication accuracyof the person, as with the above described step S316.

As a result of the determination at the step S326, when it is determinedthat the evaluation value is better (the step S326: Yes), it isestimated that the result of the recognition operation using therecognition target image 200 generated based on the image generationparameter 300 that is newly set at the step S320 is better than theresult of the recognition operation using the recognition target image200 generated based on the image generation parameter 300 that isrecorded in the parameter DB 321. In this case, the parameterdetermination unit 314 newly records, in the parameter DB 321, theinformation related to the image generation parameter 300 that is newlyset at the step S320 (the step S327). Namely, the parameterdetermination unit 314 rewrites (namely, update) the information relatedto the image generation parameter 300 that is recorded in the parameterDB 321 by the information related to the image generation parameter 300that is newly set at the step S320.

On the other hand, as a result of the determination at the step S326,when it is determined that the evaluation value is not better (the stepS326: No), it is estimated that the result of the recognition operationusing the recognition target image 200 generated based on the imagegeneration parameter 300 that is recorded in the parameter DB 321 isbetter than the result of the recognition operation using therecognition target image 200 generated based on the image generationparameter 300 that is newly set at the step S320. In this case, theparameter determination unit 314 does not newly record, in the parameterDB 321, the information related to the image generation parameter 300that is newly set at the step S320. Namely, the information related tothe image generation parameter 300 that is recorded in the parameter DB321 is kept being recorded in the parameter DB 321 as it is.

3) Technical Effect of Recognition System SYSb

As described above, the recognition system SYSb (especially, theparameter determination apparatus 3) in the second example embodiment iscapable of determining the Image generation parameter 300 by using therecognized result information indicating the recognized result of therecognition operation. Namely, the parameter determination apparatus 3is capable of determining the value of the image generation parameter300 that should be set to the recognition apparatus 2. Thus, theparameter determination apparatus 3 is capable of determining the valueof the image generation parameter 300 by which the recognition targetimage 200 that is easy to be recognized by the recognition apparatus 2is generable. Namely, the parameter determination apparatus 3 is capableof determining the value of the image generation parameter 300 that isused to generate the recognition target image 200 so that therecognition unit 211 performs the recognition operation by using therecognition target image 200 that is easy to be recognized by therecognition apparatus 2. Thus, when the parameter determinationoperation is performed, the image processing unit 212 b is capable ofgenerating the recognition target image 200 that is easier to berecognized by the recognition unit 211, compared to the case where theparameter determination operation is not performed. Furthermore, whenthe parameter determination operation is performed, the recognition unit211 is capable of performing more proper recognition operation by usingthe recognition target image 200 that is easier to be recognized by therecognition unit 211, compared to the case where the parameterdetermination operation is not performed.

Note that the parameter determination apparatus 3 may determine theimage generation parameter 300 related to the recognition apparatus 2 b(specifically, the processing parameter 303 b) after determining theimage generation parameters 300 related to the imaging apparatus 1(specifically, the optical parameter 301 and the processing parameter302). This is because the image processing unit 212 b performs the imageprocessing on the recognition target image 100 transmitted from theimaging apparatus 1, and thus, there is a possibility that the value ofthe image generation parameter 300 related to the recognition apparatus2 b is needed to be changed when the value of the image generationparameter 300 related to the imaging apparatus 1 is changed. Thus, whenthe image generation parameter 300 related to the recognition apparatus2 b is determined after the image generation parameter 300 related tothe imaging apparatus 1 is determined, the parameter determinationapparatus 3 is capable of determining the image generation parameter 300related to the recognition apparatus 2 in a situation where the value ofthe image generation parameter 300 related to the imaging apparatus 1 isfixed. Thus, the parameter determination apparatus 3 is capable ofdetermining the image generation parameter 300 relatively effectively.

Moreover, in the second example embodiment, the recognition target image300 that is generated by the image processing unit 212 b performing theimage processing on the recognition target image 100 may be stored inthe image DB 221 of the storage apparatus 22, in addition to or insteadof the recognition target image 100 obtained from the imaging apparatus1.

Moreover, as described above, the image processing unit 212 b of therecognition apparatus 2 b sometimes performs one type of imageprocessing that is same as the image processing performed by the imageprocessing unit 121 of the imaging apparatus 1. In this case theparameter determination apparatus 3 may determine the parameter thatspecifies a detail of the one type of image processing performed byeither one of the image processing units 121 and 212 b and may notdetermine the parameter that specifies a detail of the one type of imageprocessing performed by the other one of the image processing units 121and 212 b. In this case, the parameter that specifies a detail of theone type of image processing performed by the other one of the imageprocessing units 121 and 212 b may be set to be a default value (forexample, the initial value). Even in this case, the fact remains thatthe recognition target image 200 that is easy to be recognized by therecognition apparatus 2 is generated, because the parameter thatspecifies a detail of the one type of image processing performed byeither one of the image processing units 121 and 212 b is determinedbased on the recognition target image 200 that is generated by the onetype of image processing performed by the image processing unit 121 andthe one type of image processing performed by the image processing unit212 b. Alternatively, the parameter determination apparatus 3 maydetermine the parameter that specifies a detail of the one type of imageprocessing performed by the image processing unit 212 b so that theimage processing unit 212 b does not perform the one type of imageprocessing. This is because the one type of image processing isperformed by the image processing unit 121 even when the one type ofimage processing is not performed by the image processing unit 212 b,and thus, the fact remains that the recognition target image 200 that iseventually generated is the image on which the one type of imageprocessing is already performed.

Recognition System SYS in Third Example Embodiment

Next, the recognition system SYS in a third example embodiment will bedescribed. Hereinafter, the recognition system SYS in the third exampleembodiment is referred to as a “recognition system SYSc”. Therecognition system SYSc in the third example embodiment is differentfrom the recognition system SYSa in the first example embodiment in thatis includes a parameter determination apparatus 3 c instead of theparameter determination apparatus 3. Another feature of the recognitionsystem SYSc may be same as another feature of the recognition systemSYSa. Thus, in the below described description, with reference to FIG. 8, a configuration of the parameter determination apparatus 3 c in thethird example embodiment will be described. FIG. 8 is a block diagramthat illustrates the configuration of the parameter determinationapparatus 3 c in the third example embodiment. Note that a detaileddescription of the component that is already described is omitted byassigning the same reference number thereto.

As illustrated in FIG. 8 , the parameter determination apparatus 3 c isdifferent from the above described parameter determination apparatus 3in that a scene determination unit 315 c is implemented in thearithmetic apparatus 31. Another feature of the parameter determinationapparatus 3 c may be same as another feature of the parameterdetermination apparatus 3.

The scene determination unit 315 c determines a scene of the recognitiontarget image 100. For example, the scene determination unit 315 canalyzes the recognition target image 100 to determine, as the scene ofthe recognition target image 100, either one of a plurality of types ofscenes that are set in advance. Note that the scene determination unit315 c may determine the scene of the recognition target image 100 byusing an existing method of determining a scene of an image. Thus, adetailed description of the method of determining the scene of therecognition target image 100 is omitted.

The scene determined by the scene determination unit 315 c may be usedto select the image generation parameter 300 the value of which shouldbe determined by the parameter determination unit 314. Namely, theparameter determination unit 314 may select, based on the scenedetermined by the scene determination unit 315 c, at least one imagegeneration parameter 300 the value of which should be determined by theparameter determination unit 314 in the above described parameterdetermination operation from the plurality of types of image generationparameters 300.

For example, when the scene determined by the scene determination unit315 c is a foggy and / or misted scene, the parameter determination unit314 may select, as the image generation parameter 300 the value of whichshould be determined, at least one of the parameter that specifies adetail of the white balance correction processing, the parameter thatspecifies a detail of the brightness correction processing, theparameter that specifies a detail of the contrast correction processing,the parameter that specifies a detail of the dehaze processing, theparameter that specifies a detail of the HDR processing and theparameter that specifies a detail of the denoise processing. Forexample, when the scene determined by the scene determination unit 315 cis not the foggy and / or misted scene, the parameter determination unit314 may not select, as the image generation parameter 300 the value ofwhich should be determined, the parameter that specifies a detail of thedehaze processing.

For example, when the scene determined by the scene determination unit315 c is a scene in which an image of a backlit person is captured, theparameter determination unit 314 may select, as the image generationparameter 300 the value of which should be determined, at least one ofthe parameter that specifies a detail of the white balance correctionprocessing, the parameter that specifies a detail of the brightnesscorrection processing, the parameter that specifies a detail of thecontrast correction processing, the parameter that specifies a detail ofthe HDR processing and the parameter that specifies a detail of thedenoise processing. On the other hand, for example, when the scenedetermined by the scene determination unit 315 c is the scene in whichthe image of the backlit person is captured, the parameter determinationunit 314 may not select, as the image generation parameter 300 the valueof which should be determined, the parameter that specifies a detail ofthe dehaze processing. For example, when the scene determined by thescene determination unit 315 c is a scene in which an image of afollow-lighting person is captured, the parameter determination unit 314may not select, as the image generation parameter 300 the value of whichshould be determined, the parameter that specifies a detail of the HDRprocessing.

For example, when the scene determined by the scene determination unit315 c is a relatively dark scene (for example, a night), the parameterdetermination unit 314 may select, as the image generation parameter 300the value of which should be determined, at least one of the parameterthat specifies a detail of the white balance correction processing, theparameter that specifies a detail of the brightness correctionprocessing, the parameter that specifies a detail of the contrastcorrection processing, the parameter that specifies a detail of the HDRprocessing and the parameter that specifies a detail of the denoiseprocessing. On the other hand, for example, when the scene determined bythe scene determination unit 315 c is the relatively dark scene, theparameter determination unit 314 may not select, as the image generationparameter 300 the value of which should be determined, the parameterthat specifies a detail of the dehaze processing.

For example, when the scene determined by the scene determination unit315 c is a relatively light scene (for example, a daytime), theparameter determination unit 314 may select, as the image generationparameter 300 the value of which should be determined, at least one ofthe parameter that specifies a detail of the white balance correctionprocessing, the parameter that specifies a detail of the brightnesscorrection processing, the parameter that specifies a detail of thecontrast correction processing, the parameter that specifies a detail ofthe HDR processing and the parameter that specifies a detail of thedenoise processing. On the other hand, for example, when the scenedetermined by the scene determination unit 315 c is the relatively lightscene, the parameter determination unit 314 may not select, as the imagegeneration parameter 300 the value of which should be determined, theparameter that specifies a detail of the dehaze processing.

In this case, the parameter determination unit 314 may determine thevalue of at least one image generation parameter 300 that is selectedand may not determine the value of at least other one image generationparameter 300 that is not selected. The image generation parameter 300that is not selected may be set to be the default value (for example,the initial value). Alternatively, when the image generation parameter300 that is not selected is the parameter related to the imageprocessing, the image generation parameter 300 that is not selected maybe set to be a value specifying that the image processing correspondingto this image generation parameter 300 is not performed. Namely, thedefault value of the image generation parameter 300 in the third exampleembodiment may be set to be the value specifying that the imageprocessing corresponding to this image generation parameter 300 is notperformed. As a result, a processing load of the parameter determinationapparatus 3 c is reduced, compared to a case where all of the values ofthe plurality of types of the image generation parameters 300 aredetermined one by one based on the evaluation value. Namely, therecognition system SYSc is capable of reducing the processing load ofthe parameter determination apparatus 3 c while achieving an effect thatis same as the effect achievable by the recognition system SYSa.

When the image generation parameter 300 specifies a detail of the imageprocessing, the scene determined by the scene determination unit 315 cmay be used to determine an intensity of the image processingcorresponding to the image generation parameter 300. Namely, theparameter determination unit 314 may determine the value of the imagegeneration parameter 300 so that the intensity of the image processingis set to be an intensity based on the scene determined by the scenedetermination unit 315 c.

For example, when the scene determined by the scene determination unit315 c is the foggy and / or misted scene, the parameter determinationunit 314 may determine the image generation parameter 300 that specifiesa detail of the dehaze processing so that the intensity of the dehazeprocessing is set to be a relatively stronger intensity, compared to acase where the scene determined by the scene determination unit 315 c isanother scene. For example, when the scene determined by the scenedetermination unit 315 c is the scene in which the image of the backlitperson is captured, the parameter determination unit 314 may determinethe image generation parameter 300 that specifies a detail of the HDRprocessing so that the intensity of the HDR processing is set to be arelatively stronger intensity, compared to a case where the scenedetermined by the scene determination unit 315 c is another scene. Forexample, when the scene determined by the scene determination unit 315 cis the relatively dark scene (for example, the night), the parameterdetermination unit 314 may determine the image generation parameter 300that specifies at least one of the white balance correction processing,the brightness correction processing, the contrast correctionprocessing, the dehaze processing, the HDR processing and the denoiseprocessing so that the intensity of at least one of the white balancecorrection processing, the brightness correction processing, thecontrast correction processing, the dehaze processing, the HDRprocessing and the denoise processing is set to be a relatively strongerintensity, compared to a case where the scene determined by the scenedetermination unit 315 c is another scene. For example, when the scenedetermined by the scene determination unit 315 c is the relatively lightscene (for example, the daytime), the parameter determination unit 314may determine the image generation parameter 300 that specifies at leastone of the white balance correction processing, the brightnesscorrection processing and the contrast correction processing, the dehazeprocessing, the HDR processing and the denoise processing so that theintensity of at least one of the white balance correction processing,the brightness correction processing and the contrast correctionprocessing is set to be a relatively stronger intensity and theintensity of at least one of the dehaze processing, the HDR processingand the denoise processing is set to be a relatively weaker intensity,compared to a case where the scene determined by the scene determinationunit 315 c is another scene. In this case, the parameter determinationunit 314 is capable of properly determining the value of the imagegeneration parameter 300 based on the scene.

Incidentally, the feature described in the third example embodiment(specifically, the feature related to the scene determination unit 315c) may be applied to the second example embodiment.

Modified Example

In the above described description, it can be said that the parameterdetermination apparatus 3 searches the value (the candidate value) ofthe image generation parameter 300 by which the evaluation value is thebest while actually setting, to the imaging apparatus 1 or therecognition apparatus 2, all of the candidate values of the imagegeneration parameter 300 that should be set to the imaging apparatus 1or the recognition apparatus 2 in the parameter determination operation.In this case, the parameter determination apparatus 3 may repeat anoperation for fining a granularity of the candidate values of the imagegeneration parameter 300 and for searching the value of the imagegeneration parameter 300 by which the evaluation value is the best whileactually setting, to the imaging apparatus 1 or the recognitionapparatus 2, the candidate value having fined granularity each time thesearch of the value of the image generation parameter 300 by which theevaluation value is the best is completed (for example, each time it isdetermined to be Yes at the step S318 in FIG. 5 or the step S328 in FIG.7 ). For example, as illustrated in FIG. 9 , an operation for searchingthe value of the image generation parameter 300 by which the evaluationvalue is the best while actually setting, to the imaging apparatus 1 orthe recognition apparatus 2, five candidate values (specifically, fivecandidate values including 50, 100, 150, 200 and 250) related to onetype of image generation parameter 300. In this situation, it is assumedthat the candidate value “100” is searched as the value of the imagegeneration parameter 300 by which the evaluation value is the best. Inthis case, the parameter determination apparatus 3 may set a pluralityof candidate values having fined granularity around the candidate value“100” that is determined as the value of the image generation parameter300 by which the evaluation value is the best. For example, in anexample illustrated in FIG. 9 , the parameter determination apparatus 3newly sets five candidate values including 60, 80, 100, 120 and 140. Asa result, it is assumed that the candidate value “80” is searched as thevalue of the image generation parameter 300 by which the evaluationvalue is the best. In this case, the parameter determination apparatus 3may set a plurality of candidate values having more fined granularityaround the candidate value “80” that is determined as the value of theimage generation parameter 300 by which the evaluation value is thebest. For example, in an example illustrated in FIG. 9 , the parameterdetermination apparatus 3 newly sets seven candidate values including65, 70, 75, 80, 85, 90 and 90. As a result, it is assumed that thecandidate value “85” is searched as the value of the image generationparameter 300 by which the evaluation value is the best. Thus, the value“85”, by which the evaluation value is better than that of the value“100” that is determined as the value of the image generation parameter300 by which the evaluation value is the best in a situation where thegranularity of the image generation parameter 300 is fixed, isdetermined as the value of the image generation parameter 300. Thus, theparameter determination apparatus 3 is capable of the image generationparameter 300 by which the recognition target image 100 that is easierto be recognized by the recognition apparatus 2 is generable.

Moreover, in the first example embodiment to the third exampleembodiment described above, the imaging apparatus 1 includes the imageprocessing unit 121. However, the imaging apparatus 1 may not includethe image processing unit 121. In this case, the parameter determinationapparatus 3 may not determine the image generation parameter 300 of theimage processing unit 121 (namely, the processing parameter 302).Alternatively, the parameter determination apparatus 3 may not determinethe image generation parameter 300 of the image processing unit 121(namely, the processing parameter 302) even when the imaging apparatus 1includes the image processing unit 121. In this case, the processingparameter 302 of the image processing unit 121 may remain to be theinitial value. Moreover, in the first example embodiment to the thirdexample embodiment described above, the parameter determinationapparatus 3 determines the image generation parameter 300 of the camera11 (namely, the optical parameter 301). However, the parameterdetermination apparatus 3 may not determine the image generationparameter 300 of the camera 11 (namely, the optical parameter 301). Inthis case, the optical parameter 301 of the camera 11 may remain to bethe initial value.

Supplementary Note

With respect to the example embodiments described above, the followingSupplementary Notes will be further disclosed. A part or whole of theabove described example embodiments may be described as the belowdescribed Supplementary Note, however, is not limited to the following.

Supplementary Note 1

A parameter determination apparatus including:

-   a calculation unit that is configured to calculate, based on a    recognized result of a plurality of recognition target images by a    recognition apparatus that performs a recognition operation on the    recognition target image, an evaluation value for evaluating the    recognized result; and-   a determination unit that is configured to determine, based on the    evaluation value, an image generation parameter that is used to    generate the recognition target image.

Supplementary Note 2

The parameter determination apparatus according to Supplementary Note 1,wherein

-   the recognition target image is generated by an image processing    unit of the recognition apparatus performing a first image    processing on an image that is inputted from an imaging apparatus    including a camera to the recognition apparatus,-   the image generation parameter includes a first processing parameter    that specifies a detail of the first image processing.

Supplementary Note 3

The parameter determination apparatus according to Supplementary Note 1or 2, wherein

-   the recognition target image is inputted to the recognition    apparatus from an imaging apparatus including a camera,-   the image generation parameter includes an optical parameter that    specifies an optical characteristic of the camera.

Supplementary Note 4

The parameter determination apparatus according to any one ofSupplementary Notes 1 to 3, wherein

-   the recognition target image is inputted to the recognition    apparatus from an imaging apparatus including a camera and an image    processing unit that generates the recognition target image by    performing a second image processing on an image captured by the    camera,-   the image generation parameter includes a second processing    parameter that specifies a detail of the second image processing.

Supplementary Note 5

The parameter determination apparatus according to Supplementary Note 4,wherein

-   the image generation parameter includes an optical parameter that    specifies an optical characteristic of the camera,-   the determination unit determines the optical parameter and then    determines the second processing parameter.

Supplementary Note 6

The parameter determination apparatus according to any one ofSupplementary Notes 1 to 5, wherein

-   the recognition target image is inputted to the recognition    apparatus from an imaging apparatus including a camera and an image    processing unit that generates the recognition target image by    performing a second image processing on an image captured by the    camera,-   the recognition apparatus performs, by using an image processing    unit of the recognition apparatus, a first image processing on the    recognition target image inputted from the imaging apparatus, and    recognizes the recognition target image on which the first imaging    processing is performed,-   the image generation parameter includes a first processing parameter    that specifies a detail of the first image processing and a second    processing parameter that specifies a detail of the second image    processing,-   the determination unit determines the second processing parameter    and then determines the first processing parameter.

Supplementary Note 7

The parameter determination apparatus according to any one ofSupplementary Notes 1 to 6 further including a scene determination unitthat is configured to determine a scene of the recognition target image,

-   the determination unit determining at least one of a plurality of    image generation parameters that is selected based on the determined    scene, and not determining at least residual one of the plurality of    image generation parameters that is not selected.

Supplementary Note 8

The parameter determination apparatus according to any one ofSupplementary Notes 1 to 7, wherein

-   the image generation parameter specifies a detail of an image    processing that is performed to generate the recognition target    image,-   the parameter determination apparatus includes a scene determination    unit that is configured to determine a scene of the recognition    target image,-   the determination unit determines the image generation parameter so    that an intensity of the image processing becomes an intensity based    on the determined scene.

Supplementary Note 9

A parameter determination method including:

-   calculating, based on a recognized result of a plurality of    recognition target images by a recognition apparatus that recognizes    the recognition target image, an evaluation value for evaluating the    recognized result; and-   determining, based on the evaluation value, an image generation    parameter that is used to generate the recognition target image.

Supplementary Note 10

A non-transitory recording medium on which a computer program thatallows a computer to execute a parameter determination method isrecorded, the parameter determination method including:

-   calculating, based on a recognized result of a plurality of    recognition target images by a recognition apparatus that recognizes    the recognition target image, an evaluation value for evaluating the    recognized result; and-   determining, based on the evaluation value, an image generation    parameter that is used to generate the recognition target image.

Supplementary Note 11

A computer program that allows a computer to execute a parameterdetermination method, the parameter determination method including:

-   calculating, based on a recognized result of a plurality of    recognition target images by a recognition apparatus that recognizes    the recognition target image, an evaluation value for evaluating the    recognized result; and-   determining, based on the evaluation value, an image generation    parameter that is used to generate the recognition target image.

The present disclosure is allowed to be changed, if desired, withoutdeparting from the essence or spirit of the invention which can be readfrom the claims and the entire specification, and a parameterdetermination apparatus, a parameter determination method and arecording medium, which involve such changes, are also intended to bewithin the technical scope of the present disclosure.

DESCRIPTION OF REFERENCE CODES

-   SYS recognition system-   1 imaging apparatus-   11 camera-   12 arithmetic apparatus-   121 image processing unit-   2, 2 b recognition apparatus-   21 arithmetic apparatus-   211 recognition unit-   212 b image processing unit-   3 parameter determination apparatus-   31 arithmetic apparatus-   311 parameter setting unit-   312 a recognized result obtaining unit-   313 evaluation unit-   314 parameter determination unit-   100, 200 recognition target image-   101 captured image-   300 image generation parameter-   301 optical parameter-   302, 303 b processing parameter

What is claimed is:
 1. A parameter determination apparatus comprising:at least one memory configured to store instructions; and at least onefirst processor configured to execute the instructions to: calculate,based on a recognized result of a plurality of recognition target imagesby a recognition apparatus that performs a recognition operation on therecognition target image, an evaluation value for evaluating therecognized result; and determine, based on the evaluation value, animage generation parameter that is used to generate the recognitiontarget image.
 2. The parameter determination apparatus according toclaim 1, wherein the recognition target image is generated by a secondprocessor of the recognition apparatus performing a first imageprocessing on an image that is inputted from an imaging apparatusincluding a camera to the recognition apparatus, the image generationparameter includes a first processing parameter that specifies a detailof the first image processing.
 3. The parameter determination apparatusaccording to claim 1, wherein the recognition target image is inputtedto the recognition apparatus from an imaging apparatus including acamera, the image generation parameter includes an optical parameterthat specifies an optical characteristic of the camera.
 4. The parameterdetermination apparatus according to claim 1, wherein the recognitiontarget image is inputted to the recognition apparatus from an imagingapparatus including a camera and a third processor configured to executean instructions to generate the recognition target image by performing asecond image processing on an image captured by the camera, the imagegeneration parameter includes a second processing parameter thatspecifies a detail of the second image processing.
 5. The parameterdetermination apparatus according to claim 4, wherein the imagegeneration parameter includes an optical parameter that specifies anoptical characteristic of the camera, the at least one processor isconfigured to execute the instructions to determine the opticalparameter and then determine the second processing parameter.
 6. Theparameter determination apparatus according to claim 1, wherein therecognition target image is inputted to the recognition apparatus froman imaging apparatus including a camera and a third processor configuredto execute an instructions to generate the recognition target image byperforming a second image processing on an image captured by the camera,the recognition apparatus performs, by using a second processor of therecognition apparatus, a first image processing on the recognitiontarget image inputted from the imaging apparatus, and recognizes therecognition target image on which the first imaging processing isperformed, the image generation parameter includes a first processingparameter that specifies a detail of the first image processing and asecond processing parameter that specifies a detail of the second imageprocessing, the at least one processor is configured to execute theinstructions to determine the second processing parameter and thendetermine the first processing parameter.
 7. The parameter determinationapparatus according to claim 1, wherein the at least one processor isconfigured to execute the instructions: to identify a scene of therecognition target image; and to determine at least one of a pluralityof image generation parameters that is selected based on the identifiedscene, and not to determine at least residual one of the plurality ofimage generation parameters that is not selected.
 8. The parameterdetermination apparatus according to claim 1, wherein the imagegeneration parameter specifies a detail of an image processing that isperformed to generate the recognition target image, the at least oneprocessor is configured to execute the instructions: to identify a sceneof the recognition target image; and to determine the image generationparameter so that a intensity of the image processing becomes anintensity based on the identified scene.
 9. A parameter determinationmethod including: calculating, based on a recognized result of aplurality of recognition target images by a recognition apparatus thatrecognizes the recognition target image, an evaluation value forevaluating the recognized result; and determining, based on theevaluation value, an image generation parameter that is used to generatethe recognition target image.
 10. A non-transitory recording medium onwhich a computer program that allows a computer to execute an parameterdetermination method is recorded, the parameter determination methodincluding: calculating, based on a recognized result of a plurality ofrecognition target images by a recognition apparatus that recognizes therecognition target image, an evaluation value for evaluating therecognized result; and determining, based on the evaluation value, animage generation parameter that is used to generate the recognitiontarget image.